Lead Data Engineer - Databricks (Rotational night shift)

Infocepts.AI · Remote

Source and collection record

Last observed by our collector: 9 October 2026 at 1:46 am IST. This is not a manual review or a confirmation from the employer.

Employer requisition: 47242

Job type and experience labels are automated discovery categories. The employer determines eligibility. Original experience wording was not retained separately; check the current employer page.

How sourcing and classification work · Report a discrepancy

Your application evidence checklist

Mark the statements you can support after reading the employer source. This checklist stays on this page; it does not score your eligibility or submit an application.

Attendance and availability: I checked the employer page for work location, office attendance, start date and duration.Current requisition: I checked the current employer requisition and its application requirements.

0 of 2 checks marked. Keep unresolved requirements as questions for the employer.

Collected role description

Position: Lead Data Engineer - Databricks (Rotational Night Shift) Location: Pune/Nagpur/Chennai/Bangalore Type of Employment: FTE Purpose of the Position: To design, build, and optimize & support scalable data pipelines and solutions using Databricks and related technologies, to make faster, data-driven decisions as part of our clients data transformation journey. We are looking for data engineer with proficiency in data integration techniques, ETL processes and data pipeline architectures and well versed in Data Quality rules, principles and implementation. Key Result Areas and Activities: Data Pipeline Development & Support: Design, develop and optimize end to end batch and streaming data pipelines using Azure Databricks and Spark. Data Architecture Implementation: Apply Medallion Architecture to structure data layers (raw, enriched, curated). Data Quality & Governance: Ensure data accuracy, consistency, and governance using tools like Azure Purview and Unity Catalog. Performance Optimization: Optimize Spark jobs, Delta Lake tables, and SQL queries for efficiency and cost-effectiveness. Collaboration & Delivery: Work closely with analysts, architects, and business teams to deliver end-to-end data solutions.

Collected experience requirement

Above 8 years